摘要
针对粒子群优化算法的局限性,提出了一种动态改变惯性权重的粒子群算法,在优化迭代过程中,惯性权重值随粒子的位置和目标函数的性质而变化。函数测试表明,改进后的算法使收敛速度显著加快,而且不易陷入局部极值点。
Because of the limitation of particle swarm optimization, a modified particle swarm optimizer which adopted the dynamic inertia weight was proposed. The dynamic inertia weight was changed in every loop according to the particles' positions and the objective function. From experimental results it can be concluded that using a dynamic inertia weight makes the rapidity of convergence accelerate and is not easy to trap in the local extreme points.
出处
《中国机械工程》
EI
CAS
CSCD
北大核心
2005年第11期945-948,共4页
China Mechanical Engineering
基金
国家重点基础研究发展计划资助项目(2003CB716207)
关键词
粒子群
优化算法
动态惯性权重
收敛速度
particle swarm
optimization algorithm
dynamic inertia weight
rapidity of convergence
